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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-3524
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dc.contributor.authorChristen, Markus-
dc.contributor.authorNiederberger, Thomas-
dc.contributor.authorOtt, Thomas-
dc.contributor.authorAryobsei, Suleiman-
dc.contributor.authorHofstetter, Reto-
dc.date.accessioned2018-03-23T14:15:02Z-
dc.date.available2018-03-23T14:15:02Z-
dc.date.issued2015-
dc.identifier.issn2185-4106de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/4218-
dc.description.abstractMicro-texts emerging from social media platforms have become an important source for research. Automatized classification and interpretation of such micro-texts is challenging. The problem is exaggerated if the number of texts is at a medium level, making it too small for effective machine learning, but too big to be efficiently analyzed solely by humans. We present a semi-supervised learning system for micro-text classification that combines machine learning techniques with the unmatched human ability for making demanding, i.e. nonlinear decisions based on sparse data. We compare our system with human performance and a predefined optimal classifier using a validated benchmark data-set.de_CH
dc.language.isoende_CH
dc.publisherIEICEde_CH
dc.relation.ispartofNonlinear Theory and Its Applicationsde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectMiningde_CH
dc.subjectTextde_CH
dc.subjectDatade_CH
dc.subjectClusteringde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleMicro-text classification between small and big datade_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Computational Life Sciences (ICLS)de_CH
dc.identifier.doi10.1587/nolta.6.556de_CH
dc.identifier.doi10.21256/zhaw-3524-
zhaw.funding.euNode_CH
zhaw.issue4de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end569de_CH
zhaw.pages.start556de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume6de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedBio-Inspired Methods & Neuromorphic Computingde_CH
zhaw.webfeedDatalabde_CH
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Christen, M., Niederberger, T., Ott, T., Aryobsei, S., & Hofstetter, R. (2015). Micro-text classification between small and big data. Nonlinear Theory and Its Applications, 6(4), 556–569. https://doi.org/10.1587/nolta.6.556
Christen, M. et al. (2015) ‘Micro-text classification between small and big data’, Nonlinear Theory and Its Applications, 6(4), pp. 556–569. Available at: https://doi.org/10.1587/nolta.6.556.
M. Christen, T. Niederberger, T. Ott, S. Aryobsei, and R. Hofstetter, “Micro-text classification between small and big data,” Nonlinear Theory and Its Applications, vol. 6, no. 4, pp. 556–569, 2015, doi: 10.1587/nolta.6.556.
CHRISTEN, Markus, Thomas NIEDERBERGER, Thomas OTT, Suleiman ARYOBSEI und Reto HOFSTETTER, 2015. Micro-text classification between small and big data. Nonlinear Theory and Its Applications. 2015. Bd. 6, Nr. 4, S. 556–569. DOI 10.1587/nolta.6.556
Christen, Markus, Thomas Niederberger, Thomas Ott, Suleiman Aryobsei, and Reto Hofstetter. 2015. “Micro-Text Classification between Small and Big Data.” Nonlinear Theory and Its Applications 6 (4): 556–69. https://doi.org/10.1587/nolta.6.556.
Christen, Markus, et al. “Micro-Text Classification between Small and Big Data.” Nonlinear Theory and Its Applications, vol. 6, no. 4, 2015, pp. 556–69, https://doi.org/10.1587/nolta.6.556.


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